mirror of https://github.com/pjreddie/darknet.git
280 lines
10 KiB
C
280 lines
10 KiB
C
#include "convolutional_layer.h"
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#include "batchnorm_layer.h"
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#include "blas.h"
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#include <stdio.h>
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layer make_batchnorm_layer(int batch, int w, int h, int c)
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{
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fprintf(stderr, "Batch Normalization Layer: %d x %d x %d image\n", w,h,c);
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layer l = {0};
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l.type = BATCHNORM;
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l.batch = batch;
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l.h = l.out_h = h;
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l.w = l.out_w = w;
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l.c = l.out_c = c;
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l.output = calloc(h * w * c * batch, sizeof(float));
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l.delta = calloc(h * w * c * batch, sizeof(float));
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l.inputs = w*h*c;
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l.outputs = l.inputs;
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l.scales = calloc(c, sizeof(float));
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l.scale_updates = calloc(c, sizeof(float));
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l.biases = calloc(c, sizeof(float));
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l.bias_updates = calloc(c, sizeof(float));
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int i;
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for(i = 0; i < c; ++i){
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l.scales[i] = 1;
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}
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l.mean = calloc(c, sizeof(float));
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l.variance = calloc(c, sizeof(float));
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l.rolling_mean = calloc(c, sizeof(float));
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l.rolling_variance = calloc(c, sizeof(float));
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l.forward = forward_batchnorm_layer;
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l.backward = backward_batchnorm_layer;
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#ifdef GPU
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l.forward_gpu = forward_batchnorm_layer_gpu;
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l.backward_gpu = backward_batchnorm_layer_gpu;
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l.output_gpu = cuda_make_array(l.output, h * w * c * batch);
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l.delta_gpu = cuda_make_array(l.delta, h * w * c * batch);
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l.biases_gpu = cuda_make_array(l.biases, c);
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l.bias_updates_gpu = cuda_make_array(l.bias_updates, c);
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l.scales_gpu = cuda_make_array(l.scales, c);
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l.scale_updates_gpu = cuda_make_array(l.scale_updates, c);
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l.mean_gpu = cuda_make_array(l.mean, c);
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l.variance_gpu = cuda_make_array(l.variance, c);
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l.rolling_mean_gpu = cuda_make_array(l.mean, c);
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l.rolling_variance_gpu = cuda_make_array(l.variance, c);
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l.mean_delta_gpu = cuda_make_array(l.mean, c);
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l.variance_delta_gpu = cuda_make_array(l.variance, c);
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l.x_gpu = cuda_make_array(l.output, l.batch*l.outputs);
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l.x_norm_gpu = cuda_make_array(l.output, l.batch*l.outputs);
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#ifdef CUDNN
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cudnnCreateTensorDescriptor(&l.normTensorDesc);
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cudnnCreateTensorDescriptor(&l.dstTensorDesc);
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cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
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cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1);
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#endif
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#endif
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return l;
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}
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void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
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{
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int i,b,f;
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for(f = 0; f < n; ++f){
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float sum = 0;
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for(b = 0; b < batch; ++b){
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for(i = 0; i < size; ++i){
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int index = i + size*(f + n*b);
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sum += delta[index] * x_norm[index];
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}
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}
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scale_updates[f] += sum;
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}
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}
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void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
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{
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int i,j,k;
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for(i = 0; i < filters; ++i){
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mean_delta[i] = 0;
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for (j = 0; j < batch; ++j) {
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for (k = 0; k < spatial; ++k) {
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int index = j*filters*spatial + i*spatial + k;
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mean_delta[i] += delta[index];
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}
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}
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mean_delta[i] *= (-1./sqrt(variance[i] + .00001f));
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}
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}
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void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
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{
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int i,j,k;
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for(i = 0; i < filters; ++i){
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variance_delta[i] = 0;
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for(j = 0; j < batch; ++j){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + i*spatial + k;
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variance_delta[i] += delta[index]*(x[index] - mean[i]);
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}
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}
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variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.));
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}
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}
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void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
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{
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int f, j, k;
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for(j = 0; j < batch; ++j){
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for(f = 0; f < filters; ++f){
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for(k = 0; k < spatial; ++k){
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int index = j*filters*spatial + f*spatial + k;
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delta[index] = delta[index] * 1./(sqrt(variance[f] + .00001f)) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
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}
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}
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}
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}
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void resize_batchnorm_layer(layer *layer, int w, int h)
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{
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fprintf(stderr, "Not implemented\n");
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}
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void forward_batchnorm_layer(layer l, network net)
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{
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if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, net.input, 1, l.output, 1);
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copy_cpu(l.outputs*l.batch, l.output, 1, l.x, 1);
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if(net.train){
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mean_cpu(l.output, l.batch, l.out_c, l.out_h*l.out_w, l.mean);
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variance_cpu(l.output, l.mean, l.batch, l.out_c, l.out_h*l.out_w, l.variance);
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scal_cpu(l.out_c, .99, l.rolling_mean, 1);
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axpy_cpu(l.out_c, .01, l.mean, 1, l.rolling_mean, 1);
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scal_cpu(l.out_c, .99, l.rolling_variance, 1);
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axpy_cpu(l.out_c, .01, l.variance, 1, l.rolling_variance, 1);
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normalize_cpu(l.output, l.mean, l.variance, l.batch, l.out_c, l.out_h*l.out_w);
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copy_cpu(l.outputs*l.batch, l.output, 1, l.x_norm, 1);
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} else {
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normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.out_c, l.out_h*l.out_w);
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}
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scale_bias(l.output, l.scales, l.batch, l.out_c, l.out_h*l.out_w);
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add_bias(l.output, l.biases, l.batch, l.out_c, l.out_h*l.out_w);
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}
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void backward_batchnorm_layer(layer l, network net)
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{
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if(!net.train){
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l.mean = l.rolling_mean;
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l.variance = l.rolling_variance;
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}
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backward_bias(l.bias_updates, l.delta, l.batch, l.out_c, l.out_w*l.out_h);
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backward_scale_cpu(l.x_norm, l.delta, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates);
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scale_bias(l.delta, l.scales, l.batch, l.out_c, l.out_h*l.out_w);
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mean_delta_cpu(l.delta, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta);
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variance_delta_cpu(l.x, l.delta, l.mean, l.variance, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta);
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normalize_delta_cpu(l.x, l.mean, l.variance, l.mean_delta, l.variance_delta, l.batch, l.out_c, l.out_w*l.out_h, l.delta);
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if(l.type == BATCHNORM) copy_cpu(l.outputs*l.batch, l.delta, 1, net.delta, 1);
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}
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#ifdef GPU
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void pull_batchnorm_layer(layer l)
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{
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cuda_pull_array(l.scales_gpu, l.scales, l.c);
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cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.c);
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cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.c);
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}
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void push_batchnorm_layer(layer l)
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{
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cuda_push_array(l.scales_gpu, l.scales, l.c);
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cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.c);
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cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.c);
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}
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void forward_batchnorm_layer_gpu(layer l, network net)
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{
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if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, net.input_gpu, 1, l.output_gpu, 1);
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copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
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if (net.train) {
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#ifdef CUDNN
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float one = 1;
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float zero = 0;
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cudnnBatchNormalizationForwardTraining(cudnn_handle(),
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CUDNN_BATCHNORM_SPATIAL,
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&one,
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&zero,
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l.dstTensorDesc,
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l.x_gpu,
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l.dstTensorDesc,
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l.output_gpu,
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l.normTensorDesc,
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l.scales_gpu,
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l.biases_gpu,
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.01,
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l.rolling_mean_gpu,
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l.rolling_variance_gpu,
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.00001,
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l.mean_gpu,
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l.variance_gpu);
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#else
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fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu);
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fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.variance_gpu);
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scal_gpu(l.out_c, .99, l.rolling_mean_gpu, 1);
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axpy_gpu(l.out_c, .01, l.mean_gpu, 1, l.rolling_mean_gpu, 1);
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scal_gpu(l.out_c, .99, l.rolling_variance_gpu, 1);
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axpy_gpu(l.out_c, .01, l.variance_gpu, 1, l.rolling_variance_gpu, 1);
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copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
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normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
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copy_gpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1);
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scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
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#endif
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} else {
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normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.out_c, l.out_h*l.out_w);
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scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
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add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.out_c, l.out_w*l.out_h);
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}
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}
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void backward_batchnorm_layer_gpu(layer l, network net)
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{
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if(!net.train){
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l.mean_gpu = l.rolling_mean_gpu;
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l.variance_gpu = l.rolling_variance_gpu;
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}
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#ifdef CUDNN
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float one = 1;
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float zero = 0;
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cudnnBatchNormalizationBackward(cudnn_handle(),
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CUDNN_BATCHNORM_SPATIAL,
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&one,
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&zero,
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&one,
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&one,
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l.dstTensorDesc,
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l.x_gpu,
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l.dstTensorDesc,
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l.delta_gpu,
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l.dstTensorDesc,
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l.x_norm_gpu,
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l.normTensorDesc,
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l.scales_gpu,
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l.scale_updates_gpu,
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l.bias_updates_gpu,
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.00001,
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l.mean_gpu,
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l.variance_gpu);
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copy_gpu(l.outputs*l.batch, l.x_norm_gpu, 1, l.delta_gpu, 1);
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#else
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backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h);
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backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.scale_updates_gpu);
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scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.out_c, l.out_h*l.out_w);
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fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.mean_delta_gpu);
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fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.variance_delta_gpu);
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normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.out_c, l.out_w*l.out_h, l.delta_gpu);
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#endif
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if(l.type == BATCHNORM) copy_gpu(l.outputs*l.batch, l.delta_gpu, 1, net.delta_gpu, 1);
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}
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#endif
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